Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
arXiv:2606.04152v1 Announce Type: new Abstract: Large language models are reshaping research practice while quietly eroding researchers epistemic accountability. This commentary introduces PEEL - Protocols for Epistemically Engaged Literacy in AI, a working scaffolding that combines deterministic distant reading via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics and abductive reasoning. Applied to AI-generated condensations of three source texts, PEEL reveals systematic distortions in quantity, term frequency, and epistemic voice that are invisible without non-
The increased deployment and integration of large language models into research workflows necessitates methods to maintain academic rigor and accountability amidst their transformative impact.
This commentary directly addresses the critical challenge of ensuring epistemic accountability and trust in research outputs compromised by LLM-generated content, affecting the integrity of scientific progress.
The introduction of PEEL as a semiotic scaffolding provides a concrete framework and tools for researchers to actively identify and mitigate distortions introduced by AI, shifting the paradigm of AI integration from passive acceptance to active, critical engagement.
- · Epistemic accountability frameworks
- · Research integrity
- · AI ethics researchers
- · Developers of AI detection tools
- · Undocumented AI usage in research
- · Uncritical adoption of LLMs for research
- · Disinformation generated by AI
- · Researchers lacking critical AI literacy
Researchers adopt protocols like PEEL to validate AI-generated research components, leading to more transparent AI integration in academic work.
Academic institutions and publishers implement stricter guidelines for AI use, potentially requiring disclosure and validation processes for LLM-assisted research.
The development of AI systems prioritizes explainability and auditability, influencing future AI design principles to better support human oversight and accountability.
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